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Towards reliable data-based optimal and predictive control using extended DMD

IFAC-PapersOnLine 2023年第1期56卷 169-174页

作者： Manuel Schaller Karl Worthmann Friedrich Philipp Sebastian Peitz Feliks Nüske Technische Universität Ilmemau Institute of Mathematics Optimization-based Control group Germany Paderborn University Department of Computer Science Data Science for Engineering Germany Max Planck Institute for Dynamics of Complex Technical Systems Magdeburg Germany

While Koopman-based techniques like extended Dynamic Mode Decomposition are nowadays ubiquitous in the data-driven approximation of dynamical systems, quantitative error estimates were only recently established. To this end, both sources of error resulting from a finite dictionary and only finitely-many data points in the generation of the surrogate model have to be taken into account. We generalize the rigorous analysis of the approximation error to the control setting while simultaneously reducing the impact of the curse of dimensionality by using a recently proposed bilinear approach. In particular, we establish uniform bounds on the approximation error of state-dependent quantities like constraints or a performance index enabling data-based optimal and predictive control with guarantees.

关键词： Approximation error data-based dictionary size eDMD estimation error finite data Koopman predicted control projection error optimal control

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Deflection Shape Balancing: An Alternative to Modal Balancing 38th

Deflection Shape Balancing: An Alternative to Modal Balancin...

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38th IMAC, A Conference and Exposition on Structural Dynamics, 2020

作者： Morrell, W. Jason Damiano, B. Hylton, K. Jordan, C. Department of Electrical Engineering and Computer Science College of Engineering The University of Tennessee KnoxvilleTN United States Dynamic Systems Analysis Group Electrical and Electronics Systems Research Division Oak Ridge National Laboratory Oak RidgeTN United States

ISBN: (纸本)9783030477165

Rotor balancing is the final step in the rotor manufacturing process and may be necessary during rotor refurbishment. The process can be time consuming and costly, especially for supercritical monolithic rotors, impacting schedule and increasing costs. Modal balancing is a commonly used practice and is effective but requires multiple runs and at least one high-speed run. Deflection shape balancing is introduced as an alternative that may offer a more efficient method of balancing making the process faster with potentially fewer balancing runs. This paper explores the performance of the deflection shape fit balancing technique by utilizing MATLAB code simulations. © 2021, The Society for Experimental Mechanics, Inc.

关键词： MATLAB

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Generalized Correspondence Matching via Flexible Hierarchical Refinement and Patch Descriptor Distillation

arXiv

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arXiv 2024年

作者： Han, Yu Long, Ziwei Zhang, Yanting Wu, Jin Fang, Zhijun Fan, Rui The School of Computer Science and Technology Donghua University Shanghai201620 China The Department of Electronics and Computer Engineering The Hong Kong University of Science and Technology Clear Water Bay Hong Kong SAR Hong Kong Hong Kong Group The College of Electronics & Information Engineering Shanghai Research Institute for Intelligent Autonomous Systems The State Key Laboratory of Intelligent Autonomous Systems Frontiers Science Center for Intelligent Autonomous Systems Tongji University Shanghai201804 China

Correspondence matching plays a crucial role in numerous robotics applications. In comparison to conventional hand-crafted methods and recent data-driven approaches, there is significant interest in plug-and-play algorithms that make full use of pre-trained backbone networks for multi-scale feature extraction and leverage hierarchical refinement strategies to generate matched correspondences. The primary focus of this paper is to address the limitations of deep feature matching (DFM), a state-of-the-art (SoTA) plug-and-play correspondence matching approach. First, we eliminate the pre-defined threshold employed in the hierarchical refinement process of DFM by leveraging a more flexible nearest neighbor search strategy, thereby preventing the exclusion of repetitive yet valid matches during the early stages. Our second technical contribution is the integration of a patch descriptor, which extends the applicability of DFM to accommodate a wide range of backbone networks pre-trained across diverse computer vision tasks, including image classification, semantic segmentation, and stereo matching. Taking into account the practical applicability of our method in real-world robotics applications, we also propose a novel patch descriptor distillation strategy to further reduce the computational complexity of correspondence matching. Extensive experiments conducted on three public datasets demonstrate the superior performance of our proposed method. Specifically, it achieves an overall performance in terms of mean matching accuracy of 0.68, 0.92, and 0.95 with respect to the tolerances of 1, 3, and 5 pixels, respectively, on the HPatches dataset, outperforming all other SoTA algorithms. Our source code, demo video, and supplement are publicly available at ***/GCM. © 2024, CC BY.

关键词： Semantics

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A Linear Parameter-Varying Approach to Data Predictive Control

arXiv

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arXiv 2023年

作者： Verhoek, Chris Berberich, Julian Haesaert, Sofie Tóth, Roland Abbas, Hossam S. The Control Systems Group Eindhoven University of Technology Netherlands The University of Stuttgart Institute for Systems Theory and Automatic Control Germany The Institute for Electrical Engineering in Medicine Universität zu Lübeck Germany The Systems and Control Lab Institute for Computer Science and Control Hungary

By means of the linear parameter-varying (LPV) Fundamental Lemma, we derive novel data-driven predictive control (DPC) methods for LPV systems. In particular, we present output-feedback and state-feedback-based LPV-DPC methods with terminal ingredients, which guarantee exponential stability and recursive feasibility. We provide methods for the data-based computation of these terminal ingredients. Furthermore, an in-depth analysis of the application and implementation aspects of the LPV-DPC schemes is given, including application for nonlinear systems and handling noisy data. We compare and demonstrate the performance of the proposed methods in a detailed simulation example involving a nonlinear unbalanced disc system. © 2023, CC BY-NC-SA.

关键词： Predictive control systems

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Investigation of the separation zone for split-domain light field visualization

Investigation of the separation zone for split-domain light ...

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International Scientific-Professional Conference on Information Technology (IT)

作者： Kamran Javidi Maria G. Martini Peter A. Kara Department of Computer Science Wireless Multimedia and Networking Research Group School of Computer Science and Mathematics Faculty of Engineering Computing and the Environment Kingston University Penrhyn Road Campus Kingston upon Thames London UK Department of Networked Systems and Services Faculty of Electrical Engineering and Informatics Budapest University of Technology and Economics Budapest Hungary

Light field visualization commonly provides a single content over the entire field of view. However, the angularly-selective nature of the technology enables the simultaneous visualization of different contents at different viewing angles. Yet segmenting the valid viewing area comes with content interference as well in forms of separation zones, resulting invalid viewing domains. In this paper, we introduce a study on the separation zone of split-domain light field visualization. We created a static-content-based scenario in which we split the valid viewing area into two distinct domains, with a separation zone in between them. This was achieved by merging two light field contents in the middle with an instantaneous switch. The resulting visualization on a light field display was captured by a DSLR camera from two viewing distances, and it was compared to the crosstalk effect caused by insufficient angular resolution.

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Utilization of COBIT 2019 for an Advanced Strategic Evaluation of IT Risk Management in the Application of e-Government at Palembang City Governance

Utilization of COBIT 2019 for an Advanced Strategic Evaluati...

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International Conference on Informatics, Multimedia, Cyber and Information System (ICIMCIS)

作者： M. Arief Algiffary Widya Cholil Tjahjanto Yesi Novaria Kunang Zaid Amin Prihandoko Magister of Informatics Engineering Bina Darma University Palembang Indonesia Faculty of Computer Science Universitas Pembangunan Nasional Veteran Jakarta Jakarta Indonesia Intelligent Systems Research Group Bina Darma University Palembang Indonesia Informatics Engineering Bina Darma University Palembang Indonesia Faculty of Computer Science Gunadarma University Jakarta Indonesia

ISBN: (数字)9798331533243

ISBN: (纸本)9798331533250

Digitalization is transforming information, data, and activities across various sectors such as business, communication, education, and government through technologies like Artificial Intelligence, Cloud Computing, and the Internet of Things (IoT). This transformation presents new opportunities but also raises concerns about privacy and data security. Palembang City Governance is currently enhancing urban development through e-Government initiatives, and the Department of Communication and Informatics, as the driving force behind this transformation, has yet to evaluate its IT risk management processes, which are crucial for addressing system disruptions, data breaches, and cyberattacks. This study evaluates IT risk management in the e-Government application by the Department of Communication and Informatics using COBIT 2019, a comprehensive and integrated framework for managing information technology that helps organizations achieve their business objectives by leveraging technology. The processes assessed, such as APO12, have already reached a maturity level of 4 (Quantitative), while EDM03, APO13, DSS04, and DSS05 have achieved a maturity level of 3 (Defined), and BAI10 remains at a maturity level of 2 (Managed). To improve it's maturity level, as well as to ensure that all risks can be managed and to mitigate potential risks, ongoing improvement efforts and gradual enhancements to systems and performance are needed in the e-Government Management Division of the Department of Communication and Informatics of Palembang City.

关键词： Prevention and mitigation Multimedia systems Data security Urban areas Force Education Internet of Things Informatics Information technology Electronic government

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Tuning PID Controller Parameters of Automatic Voltage Regulator (AVR) Using Particle Swarm Optimization : A Comparative Study

Tuning PID Controller Parameters of Automatic Voltage Regula...

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IEEE PES Conference on Innovative Smart Grid Technologies - Middle East (ISGT Middle East)

作者： Sultan Alghamdi Awadh Ba Wazir Hassan Hadi H Awaji Abdullah Ali Alhussainy Hatem Faiz Sindi Muhyaddin Rawa Department of Electrical and Computer Engineering Smart Grids Research Group Center of Research Excellence in Renewable Energy and Power Systems King Abdulaziz University Jeddah Saudi Arabia Department of Electrical and Computer Engineering King Abdulaziz University1 Jeddah Saudi Arabia

Proportional integral derivative controller is commonly used to control devices, which is a transfer function consisting of three parameters which are: Proportional Gain (kp), Integral Gain (ki) and Derivative Gain (kd). The problem of the PID controller is that the gains (kp, ki and kd) must be chosen carefully in order to get better performance. In this paper, Particle swarm optimization (PSO) applies to obtain these gains. PID controller applies to Automatic Voltage Regulator in this paper. Furthermore, this paper presents simulation results that are used for comparing the proposed gains of PID controller and base system gains based on performance criteria in the time domain (Transient Analysis), including the overshoot, rise time, settling time, and steadystate error. In addition, this paper presents a simulation for the terminal voltage response to show the efficiency of the PSO method compared with the Genetic Algorithm (GA) method and the simulated Annealing (SA) method.

关键词： Regulators Transfer functions Automatic voltage control Steady-state Smart grids Particle swarm optimization Transient analysis

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Benchmarking Apache Spark and Hadoop MapReduce on Big Data Classification

arXiv

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arXiv 2022年

作者： Tekdogan, Taha Cakmak, Ali Department of Computer Engineering Istanbul Technical University Radar and Electronic Warfare Intelligence Systems Group ASELSAN A.S. Turkey Department of Computer Engineering Istanbul Technical University Turkey

Most of the popular Big Data analytics tools evolved to adapt their working environment to extract valuable information from a vast amount of unstructured data. The ability of data mining techniques to filter this helpful information from Big Data led to the term ‘Big Data Mining’. Shifting the scope of data from small-size, structured, and stable data to huge volume, unstructured, and quickly changing data brings many data management challenges. Different tools cope with these challenges in their own way due to their architectural limitations. There are numerous parameters to take into consideration when choosing the right data management framework based on the task at hand. In this paper, we present a comprehensive benchmark for two widely used Big Data analytics tools, namely Apache Spark and Hadoop MapReduce, on a common data mining task, i.e., classification. We employ several evaluation metrics to compare the performance of the benchmarked frameworks, such as execution time, accuracy, and scalability. These metrics are specialized to measure the performance for classification task. To the best of our knowledge, there is no previous study in the literature that employs all these metrics while taking into consideration task-specific concerns. We show that Spark is 5 times faster than MapReduce on training the model. Nevertheless, the performance of Spark degrades when the input workload gets larger. Scaling the environment by additional clusters significantly improves the performance of Spark. However, similar enhancement is not observed in Hadoop. Machine learning utility of MapReduce tend to have better accuracy scores than that of Spark, like around 2%-3%, even in small-size data sets. © 2022, CC BY.

关键词： Data mining

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Image Quality Assessment for computer Vision Based Perception Algorithms Using Edge and Structural Features

Image Quality Assessment for Computer Vision Based Perceptio...

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IEEE Region 10 International Conference TENCON

作者： V G Akshaya Rithika S Lanciya Angel A K S M Amirtha Pravin Karthi Balasubramanian Suganthi Srinivasan Rakesh Rajegowda Payal Bhattacherjee Department of Electronics and Communication Engineering Amrita School of Engineering Coimbatore Amrita Vishwa Vidyapeetham India Comfort Driving Assistance Group R & D-Computer Vision Systems Valeo India Pvt.ltd Chennai

Image Quality Assessment (IQA) is a critical task in image processing, computer vision, and other related fields, as it helps in evaluating the effects of various impairments on the Quality of Experience (QoE) of consumers. However, current IQA metrics may have limitations in evaluating image quality accurately for different types of images or under different conditions. To address this, a proposed approach involves calculating an overall image quality score based on linear combination of individual image quality metrics. This method considers multiple image features such as sharpness, contrast, color, and noise, and assigns weights to each feature based on its relative importance in determining overall image quality. By combining and weighting multiple image features, the proposed approach aims to provide a more comprehensive and accurate evaluation of image quality compared to using individual metrics alone. In this study, we focus on the importance of edge features and structure-based metrics in object detection and analyze existing No-Reference (NR) IQA metrics, including Just Noticeable Blur (JNB), Cumulative Probability Blur Detection (CPBD), Visual Quality Assessment (VQA), Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE) and No-Reference Low- Light Image Enhancement Evaluation (NLIEE), and propose a linear combination formula that combines these metrics to evaluate image. We evaluate the performance of the proposed metric on several datasets by calculating the linear combination of two, three, and all the five metrics. The weights assigned to each metric in the linear combination formula are determined through experimental analysis. The combined metric scores and MOS scores are then used to compute the Spearman's rank order correlation coefficient, which measures the monotonic relationship between two variables. A higher SROCC value indicates a stronger correlation between the combined metric and MOS scores and is used to evaluate the performanc

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csl-MTFL: Multi-task Feature Learning with Joint Correlation Structure Learning for Alzheimer’s Disease Cognitive Performance Prediction 9th

csl-MTFL: Multi-task Feature Learning with Joint Correlatio...

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19th International Conference on Advanced Data Mining and Applications, ADMA 2023

作者： Liang, Wei Zhang, Kai Cao, Peng Liu, Xiaoli Yang, Jinzhu Zaiane, Osmar R. Computer Science and Engineering Northeastern University Shenyang China Key Laboratory of Intelligent Computing in Medical Image of Ministry of Education Northeastern University Shenyang China National Frontiers Science Center for Industrial Intelligence and Systems Optimization Shenyang110819 China DAMO Academy Alibaba Group Hangzhou China Alberta Machine Intelligence Institute University of Alberta EdmontonAB Canada

ISBN: (纸本)9783031466700

Alzheimer’s disease (AD) is a common chronic neurodegenerative disease and the accurate prediction of the clinical cognitive performance is important for diagnosis and treatment. Recently, multi-task feature learning (MTFL) methods with sparsity-inducing regularization have been widely investigated on cognitive performance prediction tasks. Although they have proved to achieve improved performance compared with single-task learning, the major challenges are still not fully resolved. They involve how to capture the non-linear correlation among the tasks or features, and how to introduce the learned correlation for guiding the MTFL learning. To resolve these challenges, we introduce a correlation structure learning method through self-attention learning and sequence learning for jointly capturing the complicated but more flexible relationship for features and tasks, respectively. Moreover, we develop a dual graph regularization to encode the inherent correlation and an efficient optimization algorithm for solving the nonsmooth objective function. Extensive results on the ADNI dataset demonstrate that the proposed joint training framework outperforms existing methods and achieves state-of-the-art prediction performance of AD. Specifically, the proposed algorithm achieves an nMSE (normalized Mean Squared Error)/wR (weighted R-value) of 3.808/0.438, obtaining a relative improvement of 3.84 % /7.35 % compared with the MTFL method. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Neurodegenerative diseases

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